DWDM_Theory_IV_2021-22_R20_IT_LBRCE

• Academic Calendar

• Syllabus
• Time Tables
• IV - A 
• IV - B
• Lesson Plan
• Lecture Notes
• Unit - I ppt (click here to view)
• Unit - II
• Datamining Introduction, Motivating Challenges
• Datamining Origin & Tasks, Types of Data
• Data Quality
• Data Preprocessing
• Similarity and Dissimilarity measures (by Dr.Bhagath, CSE, LBRCE)
• Measures of Similarity and Dissimilarity
• Unit - III
• Classification - Decision Tree
• Model Overfitting
• Bayes Theorem, Naive Bayes Classifier

• Descriptive Questions (Cycle - I)
• Assignment Questions (Cycle - I)
• Assignment Marks
• Mid Objective Question Paper
• Key for Mid Objective Question Paper

• Unit - IV - Association Analysis: 
• Basic Concepts and Algorithms: Problem Definition, Frequent Item Set Generation, Apriori Principle, Apriori Algorithm
• Rule Generation, Compact Representation of Frequent Itemsets, 
• FPGrowthAlgorithm
• Unit - V - Clustering:
• ppt
• DBSCAN solved problem
• DBSCAN ASSIGNMENT LINK 
•  K-Means Solved Problem-1
• K-means Solved Problem - 2
• K -means Solved Problem - 3
• Agglomerative Hierarchical Clustering - solved problem
  
• Cycle - II
• Descriptive Questions
• Objective Questions
• Assignment Questions
• ICT Tools used
• Mid-II Question Papers
• DESCRIPTIVE - final answers - Scheme
• solution for 2(a) - Apriori
• solution for 3(a) - K means
• solution for 3(d) - Dendrogram - Hierarchical - Single link - Euclidean
• OBJECTIVE - Answers
• Mid Marks
  
• A Section
• B Section
  
• Consolidated Internal Marks
• A Section
• B Section
• External Question Paper